Date of Award


Document Type


Degree Name

Master of Science (MS)


Natural Resources

First Advisor

Jennifer Pontius

Second Advisor

Scott Merrill


The ability to accurately assess forest carbon storage is critical to understanding global carbon cycles and the effects of changes in land cover on ecological processes. However, existing methods for calculating carbon storage do not explicitly account for differences in carbon stored by different species of trees. Those methods that do reflect some of this variability, such as remotely-sensing canopy structure to estimate biomass, can be resource-intensive and difficult to reproduce over past or future time steps in order to assess change. I examined the accuracy of several carbon mapping approaches to understand how specificity of forest type classification (for example, classifying forest as "sugar maple/birch" versus simply "deciduous") affects landscape estimates of forest carbon storage in the northeastern United States. I constructed three distinct models to estimate aboveground and coarse roots forest carbon across the study region. These models varied primarily in the specificity of forest type classifications in the input maps and the corresponding carbon storage estimates used for each type. The forest classification schemes tested, from highest to lowest specificity, were: 1) relative basal area by species, 2) species association classes, and 3) coarse forest types (in accordance with IPCC (2006) guidelines). The specificity of forest type classifications in the input maps did influence results, with higher carbon storage estimates generated by models using coarser forest classifications. Maps generated by models that included relative basal area or species association classifications had similar means and standard deviations to the validation plots, as well as the highest correlations with 1000 random points from a remotely-sensed biomass map, suggesting that they better represent variability in carbon storage across the region; however, this variability was largely driven by the incorporation of stand age. Error increased at higher elevations, and decreased with higher total maple-beech-birch components. This likely reflects the dominance of low elevation hardwoods in the studies on which carbon storage estimate tables are based and demonstrates the importance of matching input estimates to region-specific studies. Current estimates of forest carbon storage from the US Forest Service predict 84-90 Mg/ha in this study area, a low value when compared with my modeled estimates of 104 Mg/ha, 108 Mg/ha, and 118 Mg/ha from the relative basal area, species association, and high IPCC models, respectively. If IPCC carbon estimates are to be applied in the northeastern US, the high end of these ranges should be used. Carbon storage estimates that consider different carbon storage capacities of different tree species are useful to explore temporal trends and relative spatial patterns in carbon storage across heterogeneous landscapes, but because of the coarse resolution and low accuracy of existing stand age maps, remotely-sensed biomass maps may be a better approach to quantify carbon stored at specific locations.



Number of Pages

77 p.